Dynamic adjustment of container stacking policy in an automated container terminal

Abstract

The decision for good stacking positions for incoming containers in an automated container terminal is an important operational problem because it affects the productivity not only for stacking but also for later retrieval. This paper proposes an online search algorithm which dynamically adjusts and optimizes a stacking policy by continuously generating variants of stacking policies and evaluating them while they are actually being applied for determining the stacking positions. Simulation experiments show that the proposed algorithm is effective in enhancing the operational productivity, while other offline optimization methods are computationally infeasible to be applied to this problem.